Overview

Brought to you by YData

Dataset statistics

Number of variables26
Number of observations7787
Missing cells20
Missing cells (%)< 0.1%
Duplicate rows4
Duplicate rows (%)0.1%
Total size in memory1.5 MiB
Average record size in memory208.0 B

Variable types

Numeric18
Categorical8

Alerts

Dataset has 4 (0.1%) duplicate rowsDuplicates
Restaurant_latitude is highly overall correlated with Delivery_location_latitudeHigh correlation
Restaurant_longitude is highly overall correlated with Delivery_location_longitudeHigh correlation
Delivery_location_latitude is highly overall correlated with Restaurant_latitudeHigh correlation
Delivery_location_longitude is highly overall correlated with Restaurant_longitudeHigh correlation
humidity is highly overall correlated with Restaurant_latitude and 1 other fieldsHigh correlation
Distance (km) is highly overall correlated with Time_subtract_2 and 5 other fieldsHigh correlation
Travel_time_minutes is highly overall correlated with Distance (km) and 6 other fieldsHigh correlation
Time_subtract_2 is highly overall correlated with Distance (km) and 6 other fieldsHigh correlation
Food_manipulation is highly overall correlated with Type_of_orderHigh correlation
food_time is highly overall correlated with Distance (km) and 5 other fieldsHigh correlation
Weather_manipulation is highly overall correlated with Weather_time and 1 other fieldsHigh correlation
Weather_time is highly overall correlated with Total_delay and 1 other fieldsHigh correlation
Vehicle_manipulation is highly overall correlated with Traffic_manipulation and 2 other fieldsHigh correlation
Vehicle_time is highly overall correlated with Traffic_manipulation and 2 other fieldsHigh correlation
Traffic_manipulation is highly overall correlated with Type_of_vehicle and 2 other fieldsHigh correlation
Traffic_time is highly overall correlated with Distance (km) and 6 other fieldsHigh correlation
Total_delay is highly overall correlated with Distance (km) and 6 other fieldsHigh correlation
Total_time is highly overall correlated with Distance (km) and 6 other fieldsHigh correlation
Traffic_labels is highly overall correlated with Time_subtract_2 and 3 other fieldsHigh correlation
Type_of_order is highly overall correlated with Food_manipulationHigh correlation
Type_of_vehicle is highly overall correlated with Traffic_manipulation and 2 other fieldsHigh correlation
weather_description is highly overall correlated with Weather_manipulationHigh correlation
Vehicle_manipulation is highly imbalanced (74.1%) Imbalance
precipitation has 7758 (99.6%) zeros Zeros
Weather_time has 3001 (38.5%) zeros Zeros
Vehicle_time has 7164 (92.0%) zeros Zeros

Reproduction

Analysis started2024-12-30 17:51:37.414407
Analysis finished2024-12-30 17:52:07.588571
Duration30.17 seconds
Software versionydata-profiling vv4.12.0
Download configurationconfig.json

Variables

Delivery_person_Age
Real number (ℝ)

Distinct22
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29.503018
Minimum15
Maximum50
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size61.0 KiB
2024-12-30T23:22:07.658512image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum15
5-th percentile21
Q125
median29
Q334
95-th percentile38
Maximum50
Range35
Interquartile range (IQR)9

Descriptive statistics

Standard deviation5.6965237
Coefficient of variation (CV)0.19308275
Kurtosis-1.1228431
Mean29.503018
Median Absolute Deviation (MAD)5
Skewness0.0030387079
Sum229740
Variance32.450383
MonotonicityNot monotonic
2024-12-30T23:22:07.754094image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
29 641
 
8.2%
38 405
 
5.2%
27 403
 
5.2%
22 395
 
5.1%
35 394
 
5.1%
21 394
 
5.1%
36 392
 
5.0%
37 389
 
5.0%
33 382
 
4.9%
25 381
 
4.9%
Other values (12) 3611
46.4%
ValueCountFrequency (%)
15 3
 
< 0.1%
20 367
4.7%
21 394
5.1%
22 395
5.1%
23 353
4.5%
24 361
4.6%
25 381
4.9%
26 362
4.6%
27 403
5.2%
28 365
4.7%
ValueCountFrequency (%)
50 2
 
< 0.1%
39 342
4.4%
38 405
5.2%
37 389
5.0%
36 392
5.0%
35 394
5.1%
34 376
4.8%
33 382
4.9%
32 354
4.5%
31 360
4.6%

Delivery_person_Ratings
Real number (ℝ)

Distinct28
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.631681
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size61.0 KiB
2024-12-30T23:22:07.856716image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q14.5
median4.7
Q34.8
95-th percentile5
Maximum6
Range5
Interquartile range (IQR)0.3

Descriptive statistics

Standard deviation0.31491201
Coefficient of variation (CV)0.067990867
Kurtosis11.478889
Mean4.631681
Median Absolute Deviation (MAD)0.1
Skewness-2.2193852
Sum36066.9
Variance0.099169573
MonotonicityNot monotonic
2024-12-30T23:22:07.958494image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
4.6 1435
18.4%
4.8 1302
16.7%
4.7 1213
15.6%
4.9 1200
15.4%
5 647
8.3%
4.5 568
 
7.3%
4.4 268
 
3.4%
4.1 261
 
3.4%
4.2 256
 
3.3%
4.3 229
 
2.9%
Other values (18) 408
 
5.2%
ValueCountFrequency (%)
1 3
< 0.1%
2.5 4
0.1%
2.6 5
0.1%
2.7 3
< 0.1%
2.8 2
 
< 0.1%
2.9 3
< 0.1%
3 1
 
< 0.1%
3.1 7
0.1%
3.2 3
< 0.1%
3.3 4
0.1%
ValueCountFrequency (%)
6 2
 
< 0.1%
5 647
8.3%
4.9 1200
15.4%
4.8 1302
16.7%
4.7 1213
15.6%
4.6 1435
18.4%
4.5 568
 
7.3%
4.4 268
 
3.4%
4.3 229
 
2.9%
4.2 256
 
3.3%

Restaurant_latitude
Real number (ℝ)

High correlation 

Distinct387
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.804078
Minimum9.957144
Maximum30.914057
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size61.0 KiB
2024-12-30T23:22:08.063625image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum9.957144
5-th percentile11.003681
Q112.975996
median18.994237
Q322.74806
95-th percentile26.913726
Maximum30.914057
Range20.956913
Interquartile range (IQR)9.772064

Descriptive statistics

Standard deviation5.4351298
Coefficient of variation (CV)0.28903996
Kurtosis-0.97789242
Mean18.804078
Median Absolute Deviation (MAD)4.360185
Skewness0.081735703
Sum146427.35
Variance29.540636
MonotonicityNot monotonic
2024-12-30T23:22:08.172311image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11.021278 54
 
0.7%
13.026279 46
 
0.6%
26.911378 46
 
0.6%
11.025083 46
 
0.6%
21.157729 45
 
0.6%
17.455894 45
 
0.6%
11.003681 44
 
0.6%
26.849596 44
 
0.6%
11.000762 44
 
0.6%
18.516216 43
 
0.6%
Other values (377) 7330
94.1%
ValueCountFrequency (%)
9.957144 7
0.1%
9.959778 4
 
0.1%
9.960846 7
0.1%
9.966783 3
 
< 0.1%
9.970717 11
0.1%
9.979186 8
0.1%
9.979363 7
0.1%
9.982834 2
 
< 0.1%
9.985497 7
0.1%
9.985697 9
0.1%
ValueCountFrequency (%)
30.914057 9
0.1%
30.905562 9
0.1%
30.902872 5
0.1%
30.899992 6
0.1%
30.899584 11
0.1%
30.895817 6
0.1%
30.895204 11
0.1%
30.893384 3
 
< 0.1%
30.893244 6
0.1%
30.893234 8
0.1%

Restaurant_longitude
Real number (ℝ)

High correlation 

Distinct387
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean76.954302
Minimum72.768726
Maximum88.433452
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size61.0 KiB
2024-12-30T23:22:08.276718image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum72.768726
5-th percentile72.792731
Q173.899315
median76.619103
Q378.04725
95-th percentile85.326967
Maximum88.433452
Range15.664726
Interquartile range (IQR)4.147935

Descriptive statistics

Standard deviation3.5998935
Coefficient of variation (CV)0.046779627
Kurtosis1.6762536
Mean76.954302
Median Absolute Deviation (MAD)1.756364
Skewness1.3146107
Sum599243.15
Variance12.959233
MonotonicityNot monotonic
2024-12-30T23:22:08.395154image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
76.995017 54
 
0.7%
80.174568 46
 
0.6%
75.789034 46
 
0.6%
77.015393 46
 
0.6%
72.768726 45
 
0.6%
78.375467 45
 
0.6%
76.975525 44
 
0.6%
75.800512 44
 
0.6%
76.981876 44
 
0.6%
73.842527 43
 
0.6%
Other values (377) 7330
94.1%
ValueCountFrequency (%)
72.768726 45
0.6%
72.768778 26
0.3%
72.771477 27
0.3%
72.772629 36
0.5%
72.772697 36
0.5%
72.774209 37
0.5%
72.778059 32
0.4%
72.778666 30
0.4%
72.789122 37
0.5%
72.789292 41
0.5%
ValueCountFrequency (%)
88.433452 9
0.1%
88.433187 10
0.1%
88.400581 10
0.1%
88.400467 8
0.1%
88.39331 7
0.1%
88.393294 5
0.1%
88.368628 9
0.1%
88.36783 8
0.1%
88.366217 7
0.1%
88.365507 12
0.2%

Delivery_location_latitude
Real number (ℝ)

High correlation 

Distinct2902
Distinct (%)37.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.86423
Minimum9.967144
Maximum31.035562
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size61.0 KiB
2024-12-30T23:22:08.517316image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum9.967144
5-th percentile11.052352
Q113.038775
median19.064237
Q322.806428
95-th percentile27.011411
Maximum31.035562
Range21.068418
Interquartile range (IQR)9.7676535

Descriptive statistics

Standard deviation5.4369807
Coefficient of variation (CV)0.2882164
Kurtosis-0.97783699
Mean18.86423
Median Absolute Deviation (MAD)4.333567
Skewness0.082453077
Sum146895.76
Variance29.560759
MonotonicityNot monotonic
2024-12-30T23:22:08.626539image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
21.237729 9
 
0.1%
21.219669 9
 
0.1%
13.076279 9
 
0.1%
19.083517 8
 
0.1%
18.673935 8
 
0.1%
12.321072 8
 
0.1%
26.91842 8
 
0.1%
13.03041 8
 
0.1%
13.023041 8
 
0.1%
22.817021 8
 
0.1%
Other values (2892) 7704
98.9%
ValueCountFrequency (%)
9.967144 1
< 0.1%
9.980717 1
< 0.1%
9.980846 1
< 0.1%
9.986783 1
< 0.1%
9.989186 1
< 0.1%
9.989363 1
< 0.1%
9.989778 1
< 0.1%
9.990717 2
< 0.1%
9.995497 1
< 0.1%
9.995697 2
< 0.1%
ValueCountFrequency (%)
31.035562 1
< 0.1%
31.035204 1
< 0.1%
31.030184 1
< 0.1%
31.029584 1
< 0.1%
31.025915 1
< 0.1%
31.025817 1
< 0.1%
31.025814 1
< 0.1%
31.025204 2
< 0.1%
31.024057 1
< 0.1%
31.023234 1
< 0.1%

Delivery_location_longitude
Real number (ℝ)

High correlation 

Distinct2902
Distinct (%)37.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean77.014455
Minimum72.778726
Maximum88.563452
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size61.0 KiB
2024-12-30T23:22:08.729692image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum72.778726
5-th percentile72.845451
Q173.956619
median76.667889
Q378.099004
95-th percentile85.396967
Maximum88.563452
Range15.784726
Interquartile range (IQR)4.1423845

Descriptive statistics

Standard deviation3.6000002
Coefficient of variation (CV)0.046744474
Kurtosis1.6780216
Mean77.014455
Median Absolute Deviation (MAD)1.740966
Skewness1.3143383
Sum599711.56
Variance12.960001
MonotonicityNot monotonic
2024-12-30T23:22:08.845726image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
72.848726 9
 
0.1%
72.842629 9
 
0.1%
80.224568 9
 
0.1%
72.90765 8
 
0.1%
74.025367 8
 
0.1%
76.664878 8
 
0.1%
75.830689 8
 
0.1%
77.690489 8
 
0.1%
77.793237 8
 
0.1%
75.974167 8
 
0.1%
Other values (2892) 7704
98.9%
ValueCountFrequency (%)
72.778726 7
0.1%
72.778778 3
< 0.1%
72.781477 2
 
< 0.1%
72.782629 2
 
< 0.1%
72.782697 1
 
< 0.1%
72.784209 7
0.1%
72.788059 2
 
< 0.1%
72.788666 2
 
< 0.1%
72.788726 2
 
< 0.1%
72.791477 1
 
< 0.1%
ValueCountFrequency (%)
88.563452 1
< 0.1%
88.563187 1
< 0.1%
88.543452 1
< 0.1%
88.530581 2
< 0.1%
88.52331 1
< 0.1%
88.523187 2
< 0.1%
88.513452 1
< 0.1%
88.510581 1
< 0.1%
88.50331 1
< 0.1%
88.503294 2
< 0.1%

Type_of_order
Categorical

High correlation 

Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size61.0 KiB
snack
1989 
drinks
1979 
meal
1945 
buffet
1874 

Length

Max length7
Median length6
Mean length6.2450238
Min length5

Characters and Unicode

Total characters48630
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowsnack
2nd rowdrinks
3rd rowbuffet
4th rowbuffet
5th rowmeal

Common Values

ValueCountFrequency (%)
snack 1989
25.5%
drinks 1979
25.4%
meal 1945
25.0%
buffet 1874
24.1%

Length

2024-12-30T23:22:08.966474image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-30T23:22:09.071165image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
snack 1989
25.5%
drinks 1979
25.4%
meal 1945
25.0%
buffet 1874
24.1%

Most occurring characters

ValueCountFrequency (%)
7787
16.0%
s 3968
 
8.2%
n 3968
 
8.2%
k 3968
 
8.2%
a 3934
 
8.1%
e 3819
 
7.9%
f 3748
 
7.7%
c 1989
 
4.1%
d 1979
 
4.1%
r 1979
 
4.1%
Other values (6) 11491
23.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 48630
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
7787
16.0%
s 3968
 
8.2%
n 3968
 
8.2%
k 3968
 
8.2%
a 3934
 
8.1%
e 3819
 
7.9%
f 3748
 
7.7%
c 1989
 
4.1%
d 1979
 
4.1%
r 1979
 
4.1%
Other values (6) 11491
23.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 48630
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
7787
16.0%
s 3968
 
8.2%
n 3968
 
8.2%
k 3968
 
8.2%
a 3934
 
8.1%
e 3819
 
7.9%
f 3748
 
7.7%
c 1989
 
4.1%
d 1979
 
4.1%
r 1979
 
4.1%
Other values (6) 11491
23.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 48630
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
7787
16.0%
s 3968
 
8.2%
n 3968
 
8.2%
k 3968
 
8.2%
a 3934
 
8.1%
e 3819
 
7.9%
f 3748
 
7.7%
c 1989
 
4.1%
d 1979
 
4.1%
r 1979
 
4.1%
Other values (6) 11491
23.6%

Type_of_vehicle
Categorical

High correlation 

Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size61.0 KiB
motorcycle
4611 
scooter
2553 
electric_scooter
615 
bicycle
 
8

Length

Max length17
Median length11
Mean length10.487222
Min length8

Characters and Unicode

Total characters81664
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowscooter
2nd rowmotorcycle
3rd rowmotorcycle
4th rowmotorcycle
5th rowscooter

Common Values

ValueCountFrequency (%)
motorcycle 4611
59.2%
scooter 2553
32.8%
electric_scooter 615
 
7.9%
bicycle 8
 
0.1%

Length

2024-12-30T23:22:09.170283image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-30T23:22:09.254025image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
motorcycle 4611
59.2%
scooter 2553
32.8%
electric_scooter 615
 
7.9%
bicycle 8
 
0.1%

Most occurring characters

ValueCountFrequency (%)
o 15558
19.1%
c 13636
16.7%
e 9017
11.0%
t 8394
10.3%
r 8394
10.3%
7787
9.5%
l 5234
 
6.4%
y 4619
 
5.7%
m 4611
 
5.6%
s 3168
 
3.9%
Other values (3) 1246
 
1.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 81664
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 15558
19.1%
c 13636
16.7%
e 9017
11.0%
t 8394
10.3%
r 8394
10.3%
7787
9.5%
l 5234
 
6.4%
y 4619
 
5.7%
m 4611
 
5.6%
s 3168
 
3.9%
Other values (3) 1246
 
1.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 81664
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 15558
19.1%
c 13636
16.7%
e 9017
11.0%
t 8394
10.3%
r 8394
10.3%
7787
9.5%
l 5234
 
6.4%
y 4619
 
5.7%
m 4611
 
5.6%
s 3168
 
3.9%
Other values (3) 1246
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 81664
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 15558
19.1%
c 13636
16.7%
e 9017
11.0%
t 8394
10.3%
r 8394
10.3%
7787
9.5%
l 5234
 
6.4%
y 4619
 
5.7%
m 4611
 
5.6%
s 3168
 
3.9%
Other values (3) 1246
 
1.5%

temperature
Real number (ℝ)

Distinct1000
Distinct (%)12.9%
Missing5
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean22.477633
Minimum7.42
Maximum29.05
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size61.0 KiB
2024-12-30T23:22:09.359899image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum7.42
5-th percentile17.23
Q119.9
median22.81
Q324.94
95-th percentile28
Maximum29.05
Range21.63
Interquartile range (IQR)5.04

Descriptive statistics

Standard deviation3.2359517
Coefficient of variation (CV)0.14396319
Kurtosis-0.0026848574
Mean22.477633
Median Absolute Deviation (MAD)2.34
Skewness-0.20667717
Sum174920.94
Variance10.471383
MonotonicityNot monotonic
2024-12-30T23:22:09.468756image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
24.94 85
 
1.1%
22.99 66
 
0.8%
24.92 65
 
0.8%
24.95 63
 
0.8%
27.99 48
 
0.6%
20.61 48
 
0.6%
24.9 48
 
0.6%
22.88 41
 
0.5%
23.03 41
 
0.5%
22.97 40
 
0.5%
Other values (990) 7237
92.9%
ValueCountFrequency (%)
7.42 2
< 0.1%
7.86 1
< 0.1%
8.2 2
< 0.1%
8.21 2
< 0.1%
8.24 2
< 0.1%
8.93 1
< 0.1%
8.97 1
< 0.1%
8.99 1
< 0.1%
9.11 2
< 0.1%
9.18 2
< 0.1%
ValueCountFrequency (%)
29.05 3
 
< 0.1%
29.04 2
 
< 0.1%
29.01 5
0.1%
29 5
0.1%
28.99 7
0.1%
28.98 4
0.1%
28.97 9
0.1%
28.96 2
 
< 0.1%
28.95 1
 
< 0.1%
28.94 3
 
< 0.1%

humidity
Real number (ℝ)

High correlation 

Distinct64
Distinct (%)0.8%
Missing5
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean64.7367
Minimum27
Maximum94
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size61.0 KiB
2024-12-30T23:22:09.572253image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum27
5-th percentile43
Q153
median65
Q373
95-th percentile91
Maximum94
Range67
Interquartile range (IQR)20

Descriptive statistics

Standard deviation14.998426
Coefficient of variation (CV)0.2316835
Kurtosis-0.71108479
Mean64.7367
Median Absolute Deviation (MAD)8
Skewness-0.083470841
Sum503781
Variance224.95277
MonotonicityNot monotonic
2024-12-30T23:22:09.809872image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
73 1014
 
13.0%
65 560
 
7.2%
60 529
 
6.8%
68 450
 
5.8%
43 448
 
5.8%
57 343
 
4.4%
44 333
 
4.3%
81 293
 
3.8%
46 274
 
3.5%
64 273
 
3.5%
Other values (54) 3265
41.9%
ValueCountFrequency (%)
27 4
 
0.1%
28 42
0.5%
29 32
0.4%
30 6
 
0.1%
31 4
 
0.1%
32 2
 
< 0.1%
37 24
0.3%
38 39
0.5%
39 32
0.4%
40 12
 
0.2%
ValueCountFrequency (%)
94 68
 
0.9%
93 201
2.6%
92 95
1.2%
91 64
 
0.8%
90 9
 
0.1%
89 10
 
0.1%
88 201
2.6%
87 33
 
0.4%
86 11
 
0.1%
85 6
 
0.1%

precipitation
Real number (ℝ)

Zeros 

Distinct13
Distinct (%)0.2%
Missing5
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean0.0011642251
Minimum0
Maximum0.48
Zeros7758
Zeros (%)99.6%
Negative0
Negative (%)0.0%
Memory size61.0 KiB
2024-12-30T23:22:09.905224image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum0.48
Range0.48
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.021637894
Coefficient of variation (CV)18.585661
Kurtosis365.24065
Mean0.0011642251
Median Absolute Deviation (MAD)0
Skewness19.039401
Sum9.06
Variance0.00046819845
MonotonicityNot monotonic
2024-12-30T23:22:09.994800image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
0 7758
99.6%
0.41 4
 
0.1%
0.45 3
 
< 0.1%
0.42 3
 
< 0.1%
0.13 3
 
< 0.1%
0.4 2
 
< 0.1%
0.44 2
 
< 0.1%
0.36 2
 
< 0.1%
0.39 1
 
< 0.1%
0.43 1
 
< 0.1%
Other values (3) 3
 
< 0.1%
(Missing) 5
 
0.1%
ValueCountFrequency (%)
0 7758
99.6%
0.13 3
 
< 0.1%
0.34 1
 
< 0.1%
0.36 2
 
< 0.1%
0.38 1
 
< 0.1%
0.39 1
 
< 0.1%
0.4 2
 
< 0.1%
0.41 4
 
0.1%
0.42 3
 
< 0.1%
0.43 1
 
< 0.1%
ValueCountFrequency (%)
0.48 1
 
< 0.1%
0.45 3
< 0.1%
0.44 2
< 0.1%
0.43 1
 
< 0.1%
0.42 3
< 0.1%
0.41 4
0.1%
0.4 2
< 0.1%
0.39 1
 
< 0.1%
0.38 1
 
< 0.1%
0.36 2
< 0.1%

weather_description
Categorical

High correlation 

Distinct10
Distinct (%)0.1%
Missing5
Missing (%)0.1%
Memory size61.0 KiB
clear sky
2669 
haze
2202 
mist
1592 
broken clouds
452 
smoke
403 
Other values (5)
464 

Length

Max length16
Median length15
Mean length6.8695708
Min length3

Characters and Unicode

Total characters53459
Distinct characters23
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowmist
2nd rowmist
3rd rowmist
4th rowbroken clouds
5th rowclear sky

Common Values

ValueCountFrequency (%)
clear sky 2669
34.3%
haze 2202
28.3%
mist 1592
20.4%
broken clouds 452
 
5.8%
smoke 403
 
5.2%
scattered clouds 287
 
3.7%
overcast clouds 69
 
0.9%
fog 48
 
0.6%
few clouds 40
 
0.5%
light rain 20
 
0.3%
(Missing) 5
 
0.1%

Length

2024-12-30T23:22:10.093365image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-30T23:22:10.198902image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
clear 2669
23.6%
sky 2669
23.6%
haze 2202
19.5%
mist 1592
14.1%
clouds 848
 
7.5%
broken 452
 
4.0%
smoke 403
 
3.6%
scattered 287
 
2.5%
overcast 69
 
0.6%
fog 48
 
0.4%
Other values (3) 80
 
0.7%

Most occurring characters

ValueCountFrequency (%)
e 6409
12.0%
s 5868
11.0%
a 5247
9.8%
c 3873
 
7.2%
3537
 
6.6%
l 3537
 
6.6%
k 3524
 
6.6%
r 3497
 
6.5%
y 2669
 
5.0%
t 2255
 
4.2%
Other values (13) 13043
24.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 53459
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 6409
12.0%
s 5868
11.0%
a 5247
9.8%
c 3873
 
7.2%
3537
 
6.6%
l 3537
 
6.6%
k 3524
 
6.6%
r 3497
 
6.5%
y 2669
 
5.0%
t 2255
 
4.2%
Other values (13) 13043
24.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 53459
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 6409
12.0%
s 5868
11.0%
a 5247
9.8%
c 3873
 
7.2%
3537
 
6.6%
l 3537
 
6.6%
k 3524
 
6.6%
r 3497
 
6.5%
y 2669
 
5.0%
t 2255
 
4.2%
Other values (13) 13043
24.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 53459
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 6409
12.0%
s 5868
11.0%
a 5247
9.8%
c 3873
 
7.2%
3537
 
6.6%
l 3537
 
6.6%
k 3524
 
6.6%
r 3497
 
6.5%
y 2669
 
5.0%
t 2255
 
4.2%
Other values (13) 13043
24.4%

Distance (km)
Real number (ℝ)

High correlation 

Distinct2098
Distinct (%)26.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.362071
Minimum1.55
Maximum59.84
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size61.0 KiB
2024-12-30T23:22:10.324671image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1.55
5-th percentile2.48
Q16.97
median12.3
Q318.19
95-th percentile27.88
Maximum59.84
Range58.29
Interquartile range (IQR)11.22

Descriptive statistics

Standard deviation8.0367889
Coefficient of variation (CV)0.6014628
Kurtosis0.97925809
Mean13.362071
Median Absolute Deviation (MAD)5.63
Skewness0.81982027
Sum104050.45
Variance64.589976
MonotonicityNot monotonic
2024-12-30T23:22:10.440392image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.41 22
 
0.3%
6.03 20
 
0.3%
2.31 20
 
0.3%
2.29 19
 
0.2%
6.97 17
 
0.2%
9.41 16
 
0.2%
7 16
 
0.2%
6.42 15
 
0.2%
2.15 15
 
0.2%
9.33 15
 
0.2%
Other values (2088) 7612
97.8%
ValueCountFrequency (%)
1.55 1
 
< 0.1%
1.57 2
 
< 0.1%
1.59 1
 
< 0.1%
1.6 4
0.1%
1.62 1
 
< 0.1%
1.64 1
 
< 0.1%
1.68 1
 
< 0.1%
1.75 7
0.1%
1.78 5
0.1%
1.79 9
0.1%
ValueCountFrequency (%)
59.84 1
 
< 0.1%
59.67 1
 
< 0.1%
58.67 1
 
< 0.1%
54.98 1
 
< 0.1%
54.78 1
 
< 0.1%
54.27 2
< 0.1%
53.86 1
 
< 0.1%
53.49 4
0.1%
49.15 1
 
< 0.1%
48.72 2
< 0.1%

Travel_time_minutes
Real number (ℝ)

High correlation 

Distinct2660
Distinct (%)34.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20.36038
Minimum6.74
Maximum83.568333
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size61.0 KiB
2024-12-30T23:22:10.552590image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum6.74
5-th percentile8.9016667
Q113.9725
median19.44
Q325.475
95-th percentile34.821667
Maximum83.568333
Range76.828333
Interquartile range (IQR)11.5025

Descriptive statistics

Standard deviation8.1800276
Coefficient of variation (CV)0.40176204
Kurtosis1.8670051
Mean20.36038
Median Absolute Deviation (MAD)5.76
Skewness0.84821219
Sum158546.28
Variance66.912852
MonotonicityNot monotonic
2024-12-30T23:22:10.674201image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15.35833333 13
 
0.2%
13.01833333 12
 
0.2%
9.36 11
 
0.1%
9.971666667 11
 
0.1%
12.51 11
 
0.1%
15.52 11
 
0.1%
16.14 10
 
0.1%
12.34333333 10
 
0.1%
11.60833333 10
 
0.1%
18.86 10
 
0.1%
Other values (2650) 7678
98.6%
ValueCountFrequency (%)
6.74 1
 
< 0.1%
6.823333333 5
0.1%
7.01 4
0.1%
7.035 1
 
< 0.1%
7.133333333 1
 
< 0.1%
7.158333333 1
 
< 0.1%
7.161666667 3
< 0.1%
7.28 1
 
< 0.1%
7.316666667 1
 
< 0.1%
7.326666667 3
< 0.1%
ValueCountFrequency (%)
83.56833333 1
< 0.1%
81.405 1
< 0.1%
79.53 1
< 0.1%
72.94166667 1
< 0.1%
72.60166667 1
< 0.1%
71.01333333 2
< 0.1%
56.15833333 1
< 0.1%
54.84333333 1
< 0.1%
52.585 1
< 0.1%
52.40833333 2
< 0.1%

Time_subtract_2
Real number (ℝ)

High correlation 

Distinct2660
Distinct (%)34.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.36038
Minimum4.74
Maximum81.568333
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size61.0 KiB
2024-12-30T23:22:10.790599image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum4.74
5-th percentile6.9016667
Q111.9725
median17.44
Q323.475
95-th percentile32.821667
Maximum81.568333
Range76.828333
Interquartile range (IQR)11.5025

Descriptive statistics

Standard deviation8.1800276
Coefficient of variation (CV)0.44552606
Kurtosis1.8670051
Mean18.36038
Median Absolute Deviation (MAD)5.76
Skewness0.84821219
Sum142972.28
Variance66.912852
MonotonicityNot monotonic
2024-12-30T23:22:10.907126image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13.35833333 13
 
0.2%
11.01833333 12
 
0.2%
7.36 11
 
0.1%
7.971666667 11
 
0.1%
10.51 11
 
0.1%
13.52 11
 
0.1%
14.14 10
 
0.1%
10.34333333 10
 
0.1%
9.608333333 10
 
0.1%
16.86 10
 
0.1%
Other values (2650) 7678
98.6%
ValueCountFrequency (%)
4.74 1
 
< 0.1%
4.823333333 5
0.1%
5.01 4
0.1%
5.035 1
 
< 0.1%
5.133333333 1
 
< 0.1%
5.158333333 1
 
< 0.1%
5.161666667 3
< 0.1%
5.28 1
 
< 0.1%
5.316666667 1
 
< 0.1%
5.326666667 3
< 0.1%
ValueCountFrequency (%)
81.56833333 1
< 0.1%
79.405 1
< 0.1%
77.53 1
< 0.1%
70.94166667 1
< 0.1%
70.60166667 1
< 0.1%
69.01333333 2
< 0.1%
54.15833333 1
< 0.1%
52.84333333 1
< 0.1%
50.585 1
< 0.1%
50.40833333 2
< 0.1%

Food_manipulation
Categorical

High correlation 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size61.0 KiB
0.05
3968 
0.1
1945 
0.08
1874 

Length

Max length4
Median length4
Mean length3.7502247
Min length3

Characters and Unicode

Total characters29203
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.05
2nd row0.05
3rd row0.08
4th row0.08
5th row0.1

Common Values

ValueCountFrequency (%)
0.05 3968
51.0%
0.1 1945
25.0%
0.08 1874
24.1%

Length

2024-12-30T23:22:11.021194image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-30T23:22:11.108022image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.05 3968
51.0%
0.1 1945
25.0%
0.08 1874
24.1%

Most occurring characters

ValueCountFrequency (%)
0 13629
46.7%
. 7787
26.7%
5 3968
 
13.6%
1 1945
 
6.7%
8 1874
 
6.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 29203
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 13629
46.7%
. 7787
26.7%
5 3968
 
13.6%
1 1945
 
6.7%
8 1874
 
6.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 29203
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 13629
46.7%
. 7787
26.7%
5 3968
 
13.6%
1 1945
 
6.7%
8 1874
 
6.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 29203
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 13629
46.7%
. 7787
26.7%
5 3968
 
13.6%
1 1945
 
6.7%
8 1874
 
6.4%

food_time
Real number (ℝ)

High correlation 

Distinct4700
Distinct (%)60.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.2783718
Minimum0.237
Maximum6.9013333
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size61.0 KiB
2024-12-30T23:22:11.206786image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0.237
5-th percentile0.41464167
Q10.76658333
median1.1246667
Q31.6386667
95-th percentile2.6610767
Maximum6.9013333
Range6.6643333
Interquartile range (IQR)0.87208333

Descriptive statistics

Standard deviation0.70768664
Coefficient of variation (CV)0.55358437
Kurtosis2.3815558
Mean1.2783718
Median Absolute Deviation (MAD)0.41766667
Skewness1.24884
Sum9954.6811
Variance0.50082038
MonotonicityNot monotonic
2024-12-30T23:22:11.320998image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.070583334 8
 
0.1%
0.866166667 8
 
0.1%
0.704833334 8
 
0.1%
0.368 7
 
0.1%
0.606666667 7
 
0.1%
0.935333334 7
 
0.1%
0.550916667 7
 
0.1%
0.788533333 7
 
0.1%
0.879 7
 
0.1%
0.667916667 7
 
0.1%
Other values (4690) 7714
99.1%
ValueCountFrequency (%)
0.237 1
 
< 0.1%
0.2505 2
< 0.1%
0.257916667 1
 
< 0.1%
0.264 1
 
< 0.1%
0.265833333 1
 
< 0.1%
0.266333333 1
 
< 0.1%
0.268083333 4
0.1%
0.26925 2
< 0.1%
0.2755 1
 
< 0.1%
0.276583333 1
 
< 0.1%
ValueCountFrequency (%)
6.901333333 1
< 0.1%
6.3524 1
< 0.1%
5.521066666 1
< 0.1%
5.415833333 1
< 0.1%
5.040833333 1
< 0.1%
4.915 1
< 0.1%
4.6635 1
< 0.1%
4.647 1
< 0.1%
4.484833333 1
< 0.1%
4.406833333 1
< 0.1%

Weather_manipulation
Categorical

High correlation 

Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size61.0 KiB
0.1
3794 
0.0
3001 
0.15
540 
0.05
452 

Length

Max length4
Median length3
Mean length3.1273918
Min length3

Characters and Unicode

Total characters24353
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.1
2nd row0.1
3rd row0.1
4th row0.05
5th row0.0

Common Values

ValueCountFrequency (%)
0.1 3794
48.7%
0.0 3001
38.5%
0.15 540
 
6.9%
0.05 452
 
5.8%

Length

2024-12-30T23:22:11.434213image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-30T23:22:11.521826image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.1 3794
48.7%
0.0 3001
38.5%
0.15 540
 
6.9%
0.05 452
 
5.8%

Most occurring characters

ValueCountFrequency (%)
0 11240
46.2%
. 7787
32.0%
1 4334
 
17.8%
5 992
 
4.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 24353
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 11240
46.2%
. 7787
32.0%
1 4334
 
17.8%
5 992
 
4.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 24353
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 11240
46.2%
. 7787
32.0%
1 4334
 
17.8%
5 992
 
4.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 24353
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 11240
46.2%
. 7787
32.0%
1 4334
 
17.8%
5 992
 
4.1%

Weather_time
Real number (ℝ)

High correlation  Zeros 

Distinct1890
Distinct (%)24.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.0771665
Minimum0
Maximum6.93875
Zeros3001
Zeros (%)38.5%
Negative0
Negative (%)0.0%
Memory size61.0 KiB
2024-12-30T23:22:11.622184image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.9585
Q31.7733333
95-th percentile3.161775
Maximum6.93875
Range6.93875
Interquartile range (IQR)1.7733333

Descriptive statistics

Standard deviation1.1072603
Coefficient of variation (CV)1.0279379
Kurtosis0.5705169
Mean1.0771665
Median Absolute Deviation (MAD)0.9585
Skewness0.91284946
Sum8387.8959
Variance1.2260255
MonotonicityNot monotonic
2024-12-30T23:22:11.736741image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 3001
38.5%
1.051 11
 
0.1%
1.409666667 10
 
0.1%
0.9075 9
 
0.1%
1.732333333 9
 
0.1%
4.3565 8
 
0.1%
2.69675 8
 
0.1%
0.879 8
 
0.1%
3.253 8
 
0.1%
1.398666667 8
 
0.1%
Other values (1880) 4707
60.4%
ValueCountFrequency (%)
0 3001
38.5%
0.241166667 2
 
< 0.1%
0.290916667 1
 
< 0.1%
0.292666667 3
 
< 0.1%
0.312 1
 
< 0.1%
0.313833333 1
 
< 0.1%
0.32325 3
 
< 0.1%
0.34575 3
 
< 0.1%
0.35225 2
 
< 0.1%
0.364416667 3
 
< 0.1%
ValueCountFrequency (%)
6.93875 2
< 0.1%
5.986750001 3
< 0.1%
5.950000001 3
< 0.1%
5.800000001 2
< 0.1%
5.245000001 1
 
< 0.1%
5.239750001 1
 
< 0.1%
5.2245 2
< 0.1%
5.19225 2
< 0.1%
5.161000001 2
< 0.1%
5.15325 2
< 0.1%

Vehicle_manipulation
Categorical

High correlation  Imbalance 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size61.0 KiB
0.0
7164 
0.03
 
615
0.1
 
8

Length

Max length4
Median length3
Mean length3.0789778
Min length3

Characters and Unicode

Total characters23976
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 7164
92.0%
0.03 615
 
7.9%
0.1 8
 
0.1%

Length

2024-12-30T23:22:11.846455image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-30T23:22:11.930818image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 7164
92.0%
0.03 615
 
7.9%
0.1 8
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 15566
64.9%
. 7787
32.5%
3 615
 
2.6%
1 8
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 23976
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 15566
64.9%
. 7787
32.5%
3 615
 
2.6%
1 8
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 23976
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 15566
64.9%
. 7787
32.5%
3 615
 
2.6%
1 8
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 23976
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 15566
64.9%
. 7787
32.5%
3 615
 
2.6%
1 8
 
< 0.1%

Vehicle_time
Real number (ℝ)

High correlation  Zeros 

Distinct552
Distinct (%)7.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.044796265
Minimum0
Maximum3.1178333
Zeros7164
Zeros (%)92.0%
Negative0
Negative (%)0.0%
Memory size61.0 KiB
2024-12-30T23:22:12.024655image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0.43975
Maximum3.1178333
Range3.1178333
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.17259619
Coefficient of variation (CV)3.8529148
Kurtosis35.084478
Mean0.044796265
Median Absolute Deviation (MAD)0
Skewness4.9580804
Sum348.82852
Variance0.029789445
MonotonicityNot monotonic
2024-12-30T23:22:12.139462image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 7164
92.0%
0.556 3
 
< 0.1%
0.4533 3
 
< 0.1%
1.21905 3
 
< 0.1%
0.4428 3
 
< 0.1%
0.2922 3
 
< 0.1%
0.4475 3
 
< 0.1%
0.51845 3
 
< 0.1%
0.33055 3
 
< 0.1%
0.2768 2
 
< 0.1%
Other values (542) 597
 
7.7%
ValueCountFrequency (%)
0 7164
92.0%
0.1447 1
 
< 0.1%
0.1503 1
 
< 0.1%
0.15475 1
 
< 0.1%
0.16085 1
 
< 0.1%
0.1653 1
 
< 0.1%
0.1705 2
 
< 0.1%
0.17325 1
 
< 0.1%
0.17455 1
 
< 0.1%
0.17465 1
 
< 0.1%
ValueCountFrequency (%)
3.117833333 1
< 0.1%
2.4125 1
< 0.1%
1.849333333 1
< 0.1%
1.710333333 1
< 0.1%
1.603 1
< 0.1%
1.488666667 1
< 0.1%
1.30695 2
< 0.1%
1.2634 1
< 0.1%
1.2542 1
< 0.1%
1.24225 1
< 0.1%

Traffic_manipulation
Categorical

High correlation 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size61.0 KiB
0.05
4611 
0.1
3168 
0.15
 
8

Length

Max length4
Median length4
Mean length3.5931681
Min length3

Characters and Unicode

Total characters27980
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.1
2nd row0.05
3rd row0.05
4th row0.05
5th row0.1

Common Values

ValueCountFrequency (%)
0.05 4611
59.2%
0.1 3168
40.7%
0.15 8
 
0.1%

Length

2024-12-30T23:22:12.366115image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-30T23:22:12.446673image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.05 4611
59.2%
0.1 3168
40.7%
0.15 8
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 12398
44.3%
. 7787
27.8%
5 4619
 
16.5%
1 3176
 
11.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 27980
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 12398
44.3%
. 7787
27.8%
5 4619
 
16.5%
1 3176
 
11.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 27980
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 12398
44.3%
. 7787
27.8%
5 4619
 
16.5%
1 3176
 
11.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 27980
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 12398
44.3%
. 7787
27.8%
5 4619
 
16.5%
1 3176
 
11.4%

Traffic_time
Real number (ℝ)

High correlation 

Distinct4044
Distinct (%)51.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.2929334
Minimum0.24116667
Maximum7.0941667
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size61.0 KiB
2024-12-30T23:22:12.542455image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0.24116667
5-th percentile0.39858333
Q10.74254167
median1.1
Q31.6460833
95-th percentile2.7856667
Maximum7.0941667
Range6.853
Interquartile range (IQR)0.90354167

Descriptive statistics

Standard deviation0.7587746
Coefficient of variation (CV)0.58686285
Kurtosis2.2459626
Mean1.2929334
Median Absolute Deviation (MAD)0.42441667
Skewness1.3075884
Sum10068.073
Variance0.5757389
MonotonicityNot monotonic
2024-12-30T23:22:12.656140image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.667916667 10
 
0.1%
0.887 8
 
0.1%
1.177166667 8
 
0.1%
1.1115 8
 
0.1%
1.013916667 7
 
0.1%
0.5255 7
 
0.1%
0.45375 7
 
0.1%
0.843 7
 
0.1%
0.676 7
 
0.1%
0.554916667 7
 
0.1%
Other values (4034) 7711
99.0%
ValueCountFrequency (%)
0.241166667 4
0.1%
0.2505 3
< 0.1%
0.258083333 2
< 0.1%
0.266333333 1
 
< 0.1%
0.268083333 4
0.1%
0.26925 1
 
< 0.1%
0.2695 1
 
< 0.1%
0.2755 1
 
< 0.1%
0.276583333 1
 
< 0.1%
0.2775 1
 
< 0.1%
ValueCountFrequency (%)
7.094166667 1
< 0.1%
6.901333333 1
< 0.1%
5.415833333 1
< 0.1%
5.0585 1
< 0.1%
4.915 1
< 0.1%
4.67675 1
< 0.1%
4.6635 1
< 0.1%
4.647 2
< 0.1%
4.625833333 1
< 0.1%
4.4135 1
< 0.1%

Total_delay
Real number (ℝ)

High correlation 

Distinct5991
Distinct (%)76.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.693268
Minimum0.501
Maximum13.969083
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size61.0 KiB
2024-12-30T23:22:12.777858image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0.501
5-th percentile1.2369833
Q12.2312083
median3.2894333
Q34.69435
95-th percentile7.52192
Maximum13.969083
Range13.468083
Interquartile range (IQR)2.4631417

Descriptive statistics

Standard deviation1.969001
Coefficient of variation (CV)0.53313244
Kurtosis1.4710321
Mean3.693268
Median Absolute Deviation (MAD)1.1917333
Skewness1.1107629
Sum28759.478
Variance3.876965
MonotonicityNot monotonic
2024-12-30T23:22:12.896334image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.208 6
 
0.1%
1.791333334 6
 
0.1%
6.607916668 5
 
0.1%
1.758 5
 
0.1%
3.39375 5
 
0.1%
3.464666667 5
 
0.1%
1.752 5
 
0.1%
4.944 5
 
0.1%
2.218333333 5
 
0.1%
4.606750001 5
 
0.1%
Other values (5981) 7735
99.3%
ValueCountFrequency (%)
0.501 1
< 0.1%
0.5385 1
< 0.1%
0.553166666 1
< 0.1%
0.575666666 1
< 0.1%
0.581833334 2
< 0.1%
0.587833334 1
< 0.1%
0.5915 1
< 0.1%
0.596166666 1
< 0.1%
0.597333334 1
< 0.1%
0.608833334 1
< 0.1%
ValueCountFrequency (%)
13.96908334 1
< 0.1%
13.8974 1
< 0.1%
13.8775 1
< 0.1%
13.50515 1
< 0.1%
13.17085 1
< 0.1%
13.09 1
< 0.1%
13.0116 1
< 0.1%
12.95233333 1
< 0.1%
12.5754 1
< 0.1%
12.5665 1
< 0.1%

Traffic_labels
Categorical

High correlation 

Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size61.0 KiB
low
4167 
very low
3346 
moderate
 
264
high
 
10

Length

Max length8
Median length3
Mean length5.31925
Min length3

Characters and Unicode

Total characters41421
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowmoderate
2nd rowvery low
3rd rowvery low
4th rowvery low
5th rowlow

Common Values

ValueCountFrequency (%)
low 4167
53.5%
very low 3346
43.0%
moderate 264
 
3.4%
high 10
 
0.1%

Length

2024-12-30T23:22:13.001012image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-30T23:22:13.085186image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
low 7513
67.5%
very 3346
30.1%
moderate 264
 
2.4%
high 10
 
0.1%

Most occurring characters

ValueCountFrequency (%)
o 7777
18.8%
l 7513
18.1%
w 7513
18.1%
e 3874
9.4%
r 3610
8.7%
v 3346
8.1%
y 3346
8.1%
3346
8.1%
m 264
 
0.6%
d 264
 
0.6%
Other values (5) 568
 
1.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 41421
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 7777
18.8%
l 7513
18.1%
w 7513
18.1%
e 3874
9.4%
r 3610
8.7%
v 3346
8.1%
y 3346
8.1%
3346
8.1%
m 264
 
0.6%
d 264
 
0.6%
Other values (5) 568
 
1.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 41421
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 7777
18.8%
l 7513
18.1%
w 7513
18.1%
e 3874
9.4%
r 3610
8.7%
v 3346
8.1%
y 3346
8.1%
3346
8.1%
m 264
 
0.6%
d 264
 
0.6%
Other values (5) 568
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 41421
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 7777
18.8%
l 7513
18.1%
w 7513
18.1%
e 3874
9.4%
r 3610
8.7%
v 3346
8.1%
y 3346
8.1%
3346
8.1%
m 264
 
0.6%
d 264
 
0.6%
Other values (5) 568
 
1.4%

Total_time
Real number (ℝ)

High correlation 

Distinct5971
Distinct (%)76.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.053648
Minimum5.451
Maximum89.72765
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size61.0 KiB
2024-12-30T23:22:13.185706image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum5.451
5-th percentile8.31679
Q114.450333
median20.913533
Q328.010708
95-th percentile39.466667
Maximum89.72765
Range84.27665
Interquartile range (IQR)13.560375

Descriptive statistics

Standard deviation9.7455797
Coefficient of variation (CV)0.4419033
Kurtosis1.3937513
Mean22.053648
Median Absolute Deviation (MAD)6.7396667
Skewness0.80530321
Sum171731.75
Variance94.976323
MonotonicityNot monotonic
2024-12-30T23:22:13.291290image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
29.492 6
 
0.1%
9.568 6
 
0.1%
19.70466666 6
 
0.1%
13.604 5
 
0.1%
35.31841667 5
 
0.1%
20.788 5
 
0.1%
29.664 5
 
0.1%
19.778 5
 
0.1%
16.96875 5
 
0.1%
10.20373333 5
 
0.1%
Other values (5961) 7734
99.3%
ValueCountFrequency (%)
5.451 1
< 0.1%
5.511 1
< 0.1%
5.691533334 1
< 0.1%
5.788 1
< 0.1%
5.832683333 2
< 0.1%
5.9235 1
< 0.1%
5.9327 1
< 0.1%
5.9413 1
< 0.1%
6.012 1
< 0.1%
6.029166666 1
< 0.1%
ValueCountFrequency (%)
89.72765 1
< 0.1%
89.72516666 1
< 0.1%
85.283 1
< 0.1%
81.58291667 1
< 0.1%
81.43573333 1
< 0.1%
79.36533333 1
< 0.1%
77.66183334 1
< 0.1%
64.99 1
< 0.1%
60.13583333 1
< 0.1%
59.71296666 1
< 0.1%

Interactions

2024-12-30T23:22:05.410441image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-30T23:21:38.932806image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-30T23:21:40.459928image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-30T23:21:42.016417image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-30T23:21:43.451557image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-30T23:21:45.124969image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-30T23:21:46.549306image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-30T23:21:48.223684image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-30T23:21:49.663681image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-30T23:21:51.273737image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-30T23:21:52.742865image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-30T23:21:54.339698image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-30T23:21:55.876566image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-30T23:21:57.558299image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-30T23:21:59.052599image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-30T23:22:00.706311image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-30T23:22:02.239237image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-30T23:22:03.882342image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-30T23:22:05.493126image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-30T23:21:39.037817image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-30T23:21:40.544114image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-30T23:21:42.098706image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-30T23:21:43.540511image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-30T23:21:45.208238image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-30T23:21:46.638636image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-30T23:21:48.303375image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-30T23:21:49.752716image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-30T23:21:51.355431image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-30T23:21:52.825343image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-30T23:21:54.424117image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-30T23:21:55.965410image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
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2024-12-30T23:22:00.070171image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-30T23:22:01.738924image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-30T23:22:03.262309image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-30T23:22:04.907598image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-30T23:22:06.415267image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-30T23:21:40.040452image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-30T23:21:41.616471image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-30T23:21:43.056810image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-30T23:21:44.697139image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-30T23:21:46.159549image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-30T23:21:47.799796image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-30T23:21:49.266651image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-30T23:21:50.861049image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-30T23:21:52.335749image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-30T23:21:53.811766image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-30T23:21:55.444963image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-30T23:21:57.002090image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-30T23:21:58.638395image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-30T23:22:00.157892image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-30T23:22:01.823657image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-30T23:22:03.347012image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-30T23:22:04.996374image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-30T23:22:06.496576image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-30T23:21:40.131414image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-30T23:21:41.699139image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-30T23:21:43.135674image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-30T23:21:44.785374image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-30T23:21:46.240004image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-30T23:21:47.886779image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-30T23:21:49.349246image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-30T23:21:50.944759image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-30T23:21:52.418778image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-30T23:21:54.022810image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-30T23:21:55.529652image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-30T23:21:57.088255image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-30T23:21:58.723249image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-30T23:22:00.241033image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-30T23:22:01.911177image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-30T23:22:03.430333image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-30T23:22:05.083416image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-30T23:22:06.571984image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-30T23:21:40.212359image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-30T23:21:41.779850image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-30T23:21:43.215035image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-30T23:21:44.874578image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-30T23:21:46.319048image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-30T23:21:47.973631image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-30T23:21:49.432730image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-30T23:21:51.026690image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-30T23:21:52.500128image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-30T23:21:54.104465image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-30T23:21:55.617568image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-30T23:21:57.303869image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-30T23:21:58.807432image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-30T23:22:00.330189image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-30T23:22:01.993081image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-30T23:22:03.518380image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-30T23:22:05.165726image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-30T23:22:06.651161image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-30T23:21:40.301675image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-30T23:21:41.862615image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-30T23:21:43.295154image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-30T23:21:44.962622image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-30T23:21:46.398098image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-30T23:21:48.059709image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-30T23:21:49.513906image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-30T23:21:51.112779image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-30T23:21:52.585057image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-30T23:21:54.185509image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-30T23:21:55.709134image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-30T23:21:57.386266image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-30T23:21:58.894133image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-30T23:22:00.412169image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-30T23:22:02.080385image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-30T23:22:03.601999image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-30T23:22:05.252356image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-30T23:22:06.731655image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-30T23:21:40.384603image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-30T23:21:41.945999image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-30T23:21:43.376744image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-30T23:21:45.051815image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-30T23:21:46.480483image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-30T23:21:48.148622image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-30T23:21:49.595854image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-30T23:21:51.196152image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-30T23:21:52.669686image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-30T23:21:54.270880image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-30T23:21:55.796017image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-30T23:21:57.481575image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-30T23:21:58.977568image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-30T23:22:00.634018image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-30T23:22:02.164135image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-30T23:22:03.809271image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-30T23:22:05.336140image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Correlations

2024-12-30T23:22:13.375658image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Delivery_person_AgeDelivery_person_RatingsRestaurant_latitudeRestaurant_longitudeDelivery_location_latitudeDelivery_location_longitudetemperaturehumidityprecipitationDistance (km)Travel_time_minutesTime_subtract_2Food_manipulationfood_timeWeather_manipulationWeather_timeVehicle_manipulationVehicle_timeTraffic_manipulationTraffic_timeTotal_delayTotal_time
Delivery_person_Age1.000-0.0950.0170.0070.0170.0070.0060.0020.0190.0060.0060.0060.0020.0070.0020.0010.001-0.0040.0060.0100.0070.007
Delivery_person_Ratings-0.0951.000-0.0120.019-0.0130.0180.0070.0200.001-0.113-0.103-0.103-0.012-0.0930.026-0.0180.0100.0010.035-0.082-0.075-0.102
Restaurant_latitude0.017-0.0121.0000.0491.0000.050-0.262-0.529-0.0850.0430.0010.0010.0110.005-0.253-0.276-0.002-0.001-0.011-0.004-0.155-0.031
Restaurant_longitude0.0070.0190.0491.0000.0491.000-0.1820.415-0.005-0.013-0.000-0.000-0.011-0.0050.0910.106-0.009-0.0020.0010.0040.0590.012
Delivery_location_latitude0.017-0.0131.0000.0491.0000.050-0.262-0.530-0.0850.0500.0070.0070.0110.010-0.254-0.275-0.002-0.000-0.011-0.000-0.151-0.025
Delivery_location_longitude0.0070.0180.0501.0000.0501.000-0.1830.414-0.005-0.0040.0090.009-0.0110.0020.0890.108-0.009-0.0010.0010.0100.0650.020
temperature0.0060.007-0.262-0.182-0.262-0.1831.000-0.2370.051-0.076-0.077-0.077-0.022-0.0760.3380.2710.0180.0130.012-0.0530.106-0.044
humidity0.0020.020-0.5290.415-0.5300.414-0.2371.0000.070-0.132-0.088-0.088-0.012-0.0760.4130.340-0.013-0.0260.005-0.0630.138-0.046
precipitation0.0190.001-0.085-0.005-0.085-0.0050.0510.0701.000-0.0100.0010.0010.0020.0030.0860.0790.0210.0180.0080.0030.0480.010
Distance (km)0.006-0.1130.043-0.0130.050-0.004-0.076-0.132-0.0101.0000.9140.914-0.0060.731-0.1780.214-0.0130.091-0.0020.6950.6590.900
Travel_time_minutes0.006-0.1030.001-0.0000.0070.009-0.077-0.0880.0010.9141.0001.000-0.0090.797-0.1440.290-0.0080.108-0.0020.7570.7510.991
Time_subtract_20.006-0.1030.001-0.0000.0070.009-0.077-0.0880.0010.9141.0001.000-0.0090.797-0.1440.290-0.0080.108-0.0020.7570.7510.991
Food_manipulation0.002-0.0120.011-0.0110.011-0.011-0.022-0.0120.002-0.006-0.009-0.0091.0000.546-0.017-0.0180.0250.0190.0150.0090.1910.031
food_time0.007-0.0930.005-0.0050.0100.002-0.076-0.0760.0030.7310.7970.7970.5461.000-0.1250.2220.0050.0960.0130.6160.7300.816
Weather_manipulation0.0020.026-0.2530.091-0.2540.0890.3380.4130.086-0.178-0.144-0.144-0.017-0.1251.0000.8240.0190.0030.022-0.0920.383-0.044
Weather_time0.001-0.018-0.2760.106-0.2750.1080.2710.3400.0790.2140.2900.290-0.0180.2220.8241.0000.0150.0570.0250.2430.7400.393
Vehicle_manipulation0.0010.010-0.002-0.009-0.002-0.0090.018-0.0130.021-0.013-0.008-0.0080.0250.0050.0190.0151.0000.9080.3650.2090.1700.027
Vehicle_time-0.0040.001-0.001-0.002-0.000-0.0010.013-0.0260.0180.0910.1080.1080.0190.0960.0030.0570.9081.0000.3320.3160.2760.146
Traffic_manipulation0.0060.035-0.0110.001-0.0110.0010.0120.0050.008-0.002-0.002-0.0020.0150.0130.0220.0250.3650.3321.0000.5950.2770.054
Traffic_time0.010-0.082-0.0040.004-0.0000.010-0.053-0.0630.0030.6950.7570.7570.0090.616-0.0920.2430.2090.3160.5951.0000.7710.791
Total_delay0.007-0.075-0.1550.059-0.1510.0650.1060.1380.0480.6590.7510.7510.1910.7300.3830.7400.1700.2760.2770.7711.0000.832
Total_time0.007-0.102-0.0310.012-0.0250.020-0.044-0.0460.0100.9000.9910.9910.0310.816-0.0440.3930.0270.1460.0540.7910.8321.000
2024-12-30T23:22:13.582655image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Delivery_person_AgeDelivery_person_RatingsRestaurant_latitudeRestaurant_longitudeDelivery_location_latitudeDelivery_location_longitudetemperaturehumidityprecipitationDistance (km)Travel_time_minutesTime_subtract_2Food_manipulationfood_timeWeather_manipulationWeather_timeVehicle_manipulationVehicle_timeTraffic_manipulationTraffic_timeTotal_delayTotal_time
Delivery_person_Age1.000-0.1000.0200.0040.0200.0050.0060.0020.0190.0080.0070.0070.0020.0040.0020.0010.0050.0040.0060.0100.0100.007
Delivery_person_Ratings-0.1001.000-0.0080.021-0.0140.0150.0050.013-0.002-0.084-0.077-0.077-0.004-0.0650.013-0.0080.0170.0160.054-0.029-0.046-0.074
Restaurant_latitude0.020-0.0081.000-0.1170.997-0.116-0.248-0.480-0.0860.003-0.058-0.0580.010-0.036-0.217-0.288-0.003-0.003-0.013-0.053-0.169-0.081
Restaurant_longitude0.0040.021-0.1171.000-0.1190.996-0.1780.4820.0060.0090.0460.046-0.0080.0320.1310.214-0.006-0.0050.0110.0410.1310.061
Delivery_location_latitude0.020-0.0140.997-0.1191.000-0.112-0.252-0.487-0.0860.062-0.003-0.0030.0090.009-0.225-0.276-0.003-0.003-0.013-0.010-0.128-0.026
Delivery_location_longitude0.0050.015-0.1160.996-0.1121.000-0.1810.4710.0060.0750.1080.108-0.0080.0830.1080.214-0.007-0.0050.0110.0900.1720.122
temperature0.0060.005-0.248-0.178-0.252-0.1811.000-0.1970.061-0.057-0.038-0.038-0.024-0.0510.3410.2760.0160.0150.012-0.0240.120-0.008
humidity0.0020.013-0.4800.482-0.4870.471-0.1971.0000.075-0.130-0.100-0.100-0.011-0.0870.3950.399-0.012-0.0140.001-0.0740.144-0.060
precipitation0.019-0.002-0.0860.006-0.0860.0060.0610.0751.000-0.012-0.002-0.0020.0050.0040.0890.0640.0260.0260.0060.0040.0390.006
Distance (km)0.008-0.0840.0030.0090.0620.075-0.057-0.130-0.0121.0000.9390.939-0.0060.774-0.1700.149-0.0110.002-0.0030.7410.6940.928
Travel_time_minutes0.007-0.077-0.0580.046-0.0030.108-0.038-0.100-0.0020.9391.0001.000-0.0080.821-0.1420.210-0.0100.004-0.0010.7890.7630.993
Time_subtract_20.007-0.077-0.0580.046-0.0030.108-0.038-0.100-0.0020.9391.0001.000-0.0080.821-0.1420.210-0.0100.004-0.0010.7890.7630.993
Food_manipulation0.002-0.0040.010-0.0080.009-0.008-0.024-0.0110.005-0.006-0.008-0.0081.0000.538-0.018-0.0150.0180.0180.0140.0040.1950.030
food_time0.004-0.065-0.0360.0320.0090.083-0.051-0.0870.0040.7740.8210.8210.5381.000-0.1280.1680.0030.0140.0070.6580.7480.837
Weather_manipulation0.0020.013-0.2170.131-0.2250.1080.3410.3950.089-0.170-0.142-0.142-0.018-0.1281.0000.8810.0210.0200.022-0.0970.381-0.050
Weather_time0.001-0.008-0.2880.214-0.2760.2140.2760.3990.0640.1490.2100.210-0.0150.1680.8811.0000.0150.0190.0250.1860.6560.299
Vehicle_manipulation0.0050.017-0.003-0.006-0.003-0.0070.016-0.0120.026-0.011-0.010-0.0100.0180.0030.0210.0151.0000.9990.3580.2050.1520.022
Vehicle_time0.0040.016-0.003-0.005-0.003-0.0050.015-0.0140.0260.0020.0040.0040.0180.0140.0200.0190.9991.0000.3580.2150.1630.036
Traffic_manipulation0.0060.054-0.0130.011-0.0130.0110.0120.0010.006-0.003-0.001-0.0010.0140.0070.0220.0250.3580.3581.0000.5900.2730.050
Traffic_time0.010-0.029-0.0530.041-0.0100.090-0.024-0.0740.0040.7410.7890.7890.0040.658-0.0970.1860.2050.2150.5901.0000.7800.817
Total_delay0.010-0.046-0.1690.131-0.1280.1720.1200.1440.0390.6940.7630.7630.1950.7480.3810.6560.1520.1630.2730.7801.0000.832
Total_time0.007-0.074-0.0810.061-0.0260.122-0.008-0.0600.0060.9280.9930.9930.0300.837-0.0500.2990.0220.0360.0500.8170.8321.000
2024-12-30T23:22:13.782339image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Delivery_location_latitudeDelivery_location_longitudeDelivery_person_AgeDelivery_person_RatingsDistance (km)Food_manipulationRestaurant_latitudeRestaurant_longitudeTime_subtract_2Total_delayTotal_timeTraffic_labelsTraffic_manipulationTraffic_timeTravel_time_minutesType_of_orderType_of_vehicleVehicle_manipulationVehicle_timeWeather_manipulationWeather_timefood_timehumidityprecipitationtemperatureweather_description
Delivery_location_latitude1.000-0.1120.020-0.0140.0620.0000.997-0.119-0.003-0.128-0.0260.1120.017-0.010-0.0030.0000.0120.005-0.0030.482-0.2760.009-0.487-0.086-0.2520.367
Delivery_location_longitude-0.1121.0000.0050.0150.0750.008-0.1160.9960.1080.1720.1220.1260.0000.0900.1080.0000.0000.000-0.0050.3560.2140.0830.4710.006-0.1810.317
Delivery_person_Age0.0200.0051.000-0.1000.0080.0070.0200.0040.0070.0100.0070.0200.1470.0100.0070.0000.1200.1470.0040.0000.0010.0040.0020.0190.0060.000
Delivery_person_Ratings-0.0140.015-0.1001.000-0.0840.003-0.0080.021-0.077-0.046-0.0740.0630.158-0.029-0.0770.0000.1290.1450.0160.000-0.008-0.0650.013-0.0020.0050.014
Distance (km)0.0620.0750.008-0.0841.0000.0000.0030.0090.9390.6940.9280.4610.0000.7410.9390.0000.0000.0000.0020.1410.1490.774-0.130-0.012-0.0570.104
Food_manipulation0.0000.0080.0070.0030.0001.0000.0000.0080.0000.1360.0210.0000.0200.0000.0001.0000.0200.0220.0150.0070.0170.3930.0000.0100.0090.000
Restaurant_latitude0.997-0.1160.020-0.0080.0030.0001.000-0.117-0.058-0.169-0.0810.1110.018-0.053-0.0580.0000.0120.007-0.0030.485-0.288-0.036-0.480-0.086-0.2480.370
Restaurant_longitude-0.1190.9960.0040.0210.0090.008-0.1171.0000.0460.1310.0610.0960.0000.0410.0460.0000.0000.000-0.0050.3540.2140.0320.4820.006-0.1780.339
Time_subtract_2-0.0030.1080.007-0.0770.9390.000-0.0580.0461.0000.7630.9930.5770.0000.7891.0000.0000.0000.0000.0040.1080.2100.821-0.100-0.002-0.0380.095
Total_delay-0.1280.1720.010-0.0460.6940.136-0.1690.1310.7631.0000.8320.4920.1970.7800.7630.1100.1690.1300.1630.2400.6560.7480.1440.0390.1200.165
Total_time-0.0260.1220.007-0.0740.9280.021-0.0810.0610.9930.8321.0000.6410.0330.8170.9930.0150.0290.0140.0360.0890.2990.837-0.0600.006-0.0080.083
Traffic_labels0.1120.1260.0200.0630.4610.0000.1110.0960.5770.4920.6411.0000.3380.8990.5770.0000.2770.1400.3170.0670.3100.4190.0870.0000.2030.094
Traffic_manipulation0.0170.0000.1470.1580.0000.0200.0180.0000.0000.1970.0330.3381.0000.4310.0000.0191.0000.7500.6130.0180.0000.0130.0000.0000.0000.000
Traffic_time-0.0100.0900.010-0.0290.7410.000-0.0530.0410.7890.7800.8170.8990.4311.0000.7890.0000.3520.1600.2150.0760.1860.658-0.0740.004-0.0240.068
Travel_time_minutes-0.0030.1080.007-0.0770.9390.000-0.0580.0461.0000.7630.9930.5770.0000.7891.0000.0000.0000.0000.0040.1080.2100.821-0.100-0.002-0.0380.095
Type_of_order0.0000.0000.0000.0000.0001.0000.0000.0000.0000.1100.0150.0000.0190.0000.0001.0000.0140.0190.0000.0000.0180.3210.0000.0070.0000.000
Type_of_vehicle0.0120.0000.1200.1290.0000.0200.0120.0000.0000.1690.0290.2771.0000.3520.0000.0141.0001.0000.6950.0140.0070.0020.0000.0070.0000.014
Vehicle_manipulation0.0050.0000.1470.1450.0000.0220.0070.0000.0000.1300.0140.1400.7500.1600.0000.0191.0001.0000.8510.0180.0000.0000.0000.0090.0000.023
Vehicle_time-0.003-0.0050.0040.0160.0020.015-0.003-0.0050.0040.1630.0360.3170.6130.2150.0040.0000.6950.8511.0000.0310.0190.014-0.0140.0260.0150.026
Weather_manipulation0.4820.3560.0000.0000.1410.0070.4850.3540.1080.2400.0890.0670.0180.0760.1080.0000.0140.0180.0311.0000.6000.0930.4490.1030.3911.000
Weather_time-0.2760.2140.001-0.0080.1490.017-0.2880.2140.2100.6560.2990.3100.0000.1860.2100.0180.0070.0000.0190.6001.0000.1680.3990.0640.2760.381
food_time0.0090.0830.004-0.0650.7740.393-0.0360.0320.8210.7480.8370.4190.0130.6580.8210.3210.0020.0000.0140.0930.1681.000-0.0870.004-0.0510.076
humidity-0.4870.4710.0020.013-0.1300.000-0.4800.482-0.1000.144-0.0600.0870.000-0.074-0.1000.0000.0000.000-0.0140.4490.399-0.0871.0000.075-0.1970.361
precipitation-0.0860.0060.019-0.002-0.0120.010-0.0860.006-0.0020.0390.0060.0000.0000.004-0.0020.0070.0070.0090.0260.1030.0640.0040.0751.0000.0610.490
temperature-0.252-0.1810.0060.005-0.0570.009-0.248-0.178-0.0380.120-0.0080.2030.000-0.024-0.0380.0000.0000.0000.0150.3910.276-0.051-0.1970.0611.0000.291
weather_description0.3670.3170.0000.0140.1040.0000.3700.3390.0950.1650.0830.0940.0000.0680.0950.0000.0140.0230.0261.0000.3810.0760.3610.4900.2911.000

Missing values

2024-12-30T23:22:06.858573image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
A simple visualization of nullity by column.
2024-12-30T23:22:07.283460image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-12-30T23:22:07.512672image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

Delivery_person_AgeDelivery_person_RatingsRestaurant_latitudeRestaurant_longitudeDelivery_location_latitudeDelivery_location_longitudeType_of_orderType_of_vehicletemperaturehumidityprecipitationweather_descriptionDistance (km)Travel_time_minutesTime_subtract_2Food_manipulationfood_timeWeather_manipulationWeather_timeVehicle_manipulationVehicle_timeTraffic_manipulationTraffic_timeTotal_delayTraffic_labelsTotal_time
0344.512.91304177.68323713.04304177.813237snackscooter19.5093.00.0mist37.1745.56500043.5650000.052.1782500.104.3565000.00.00.104.35650010.891250moderate54.456250
1234.412.91426477.67840012.92426477.688400drinksmotorcycle20.4591.00.0mist3.3412.58666710.5866670.050.5293330.101.0586670.00.00.050.5293332.117333very low12.704000
2384.711.00366976.97649411.05366977.026494buffetmotorcycle23.8678.00.0mist10.0519.11666717.1166670.081.3693330.101.7116670.00.00.050.8558333.936833very low21.053500
3224.817.43166878.40832117.46166878.438321buffetmotorcycle21.4365.00.0broken clouds11.3019.07500017.0750000.081.3660000.050.8537500.00.00.050.8537503.073500very low20.148500
4334.723.36974685.33982023.47974685.449820mealscooter17.5169.00.0clear sky19.1119.63500017.6350000.101.7635000.000.0000000.00.00.101.7635003.527000low21.162000
5354.612.35205876.60665012.48205876.736650mealmotorcycle18.0382.00.0scattered clouds28.4135.20000033.2000000.103.3200000.000.0000000.00.00.051.6600004.980000low38.180000
6224.817.43380978.38674417.56380978.516744buffetmotorcycle21.0865.00.0broken clouds31.6238.54666736.5466670.082.9237330.051.8273330.00.00.051.8273336.578400low43.125067
7214.710.00306476.30758910.04306476.347589mealmotorcycle23.9588.00.0mist7.5717.25833315.2583330.101.5258330.101.5258330.00.00.050.7629173.814583very low19.072917
8234.718.56245073.91661918.65245074.006619drinksscooter19.3765.00.0clear sky21.9323.41166721.4116670.051.0705830.000.0000000.00.00.102.1411673.211750low24.623417
9344.330.89958475.80934630.91958475.829346buffetmotorcycle19.1455.00.0clear sky3.9813.23833311.2383330.080.8990670.000.0000000.00.00.050.5619171.460983very low12.699317
Delivery_person_AgeDelivery_person_RatingsRestaurant_latitudeRestaurant_longitudeDelivery_location_latitudeDelivery_location_longitudeType_of_orderType_of_vehicletemperaturehumidityprecipitationweather_descriptionDistance (km)Travel_time_minutesTime_subtract_2Food_manipulationfood_timeWeather_manipulationWeather_timeVehicle_manipulationVehicle_timeTraffic_manipulationTraffic_timeTotal_delayTraffic_labelsTotal_time
7777224.619.12008372.90738519.21008372.997385snackmotorcycle28.0457.00.0smoke18.5920.67500018.6750000.050.9337500.152.8012500.00.00.050.9337504.668750very low23.343750
7778214.121.18343472.81449221.26343472.894492snackmotorcycle24.9341.00.0clear sky16.7324.72333322.7233330.051.1361670.000.0000000.00.00.051.1361672.272333low24.995667
7779224.619.12008372.90738519.25008373.037385mealscooter27.9951.00.0clear sky26.8128.07500026.0750000.102.6075000.000.0000000.00.00.102.6075005.215000low31.290000
7780354.022.53899988.32233722.57899988.362337drinksmotorcycle27.9765.00.0haze7.6616.47333314.4733330.050.7236670.101.4473330.00.00.050.7236672.894667very low17.368000
7781285.023.33301785.31720023.39301785.377200mealmotorcycle20.0383.00.0mist10.2217.45000015.4500000.101.5450000.101.5450000.00.00.050.7725003.862500very low19.312500
7782254.019.09145872.82780819.10145872.837808mealmotorcycle28.0357.00.0smoke3.789.2100007.2100000.100.7210000.151.0815000.00.00.050.3605002.163000very low9.373000
7783324.222.31023773.15892122.40023773.248921mealmotorcycle23.9664.00.0haze18.9229.54333327.5433330.102.7543330.102.7543330.00.00.051.3771676.885833low34.429167
7784364.717.48321678.55211117.49321678.562111mealmotorcycle22.9460.00.0haze2.649.3600007.3600000.100.7360000.100.7360000.00.00.050.3680001.840000very low9.200000
7785374.626.91398775.75289127.05398775.892891buffetscooter23.7231.00.0clear sky28.8031.46833329.4683330.082.3574670.000.0000000.00.00.102.9468335.304300low34.772633
7786294.718.99423772.82555319.08423772.915553mealscooter28.0157.00.0smoke17.6321.63666719.6366670.101.9636670.152.9455000.00.00.101.9636676.872833low26.509500

Duplicate rows

Most frequently occurring

Delivery_person_AgeDelivery_person_RatingsRestaurant_latitudeRestaurant_longitudeDelivery_location_latitudeDelivery_location_longitudeType_of_orderType_of_vehicletemperaturehumidityprecipitationweather_descriptionDistance (km)Travel_time_minutesTime_subtract_2Food_manipulationfood_timeWeather_manipulationWeather_timeVehicle_manipulationVehicle_timeTraffic_manipulationTraffic_timeTotal_delayTraffic_labelsTotal_time# duplicates
0254.812.32322576.63002812.35322576.660028mealmotorcycle18.2581.00.0broken clouds6.7313.41166711.4116670.101.1411670.050.5705830.000.0000.050.5705832.282333very low13.6940002
1274.912.32319476.63058312.33319476.640583snackmotorcycle20.5467.00.0clear sky3.679.6616677.6616670.050.3830830.000.0000000.000.0000.050.3830830.766167very low8.4278332
2294.617.41233078.44965417.46233078.499654snackscooter21.4064.00.0broken clouds10.2218.16166716.1616670.050.8080830.050.8080830.000.0000.101.6161673.232333low19.3940002
3294.617.45589478.37546717.48589478.405467snackelectric_scooter21.2765.00.0broken clouds9.4520.53333318.5333330.050.9266670.050.9266670.030.5560.101.8533334.262667low22.7960002